import tensorflow as tf
import import_data

mnist = import_data.read_data_sets("./", one_hot=True)

# Define variables
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# Softmax(Wx+b) neural network
y = tf.nn.softmax(tf.matmul(x,W) + b)

# Cross-entropy function for training evaluation
y_ = tf.placeholder("float", [None, 10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init = tf.initialize_all_variables()
session = tf.Session()
session.run(init)

# Train by stochastic gradient descent
for i in xrange(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  session.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

# Evaluate our training
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print session.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
